000889218 001__ 889218
000889218 005__ 20240313094930.0
000889218 037__ $$aFZJ-2021-00123
000889218 041__ $$aEnglish
000889218 1001_ $$0P:(DE-Juel1)144807$$aDenker, Michael$$b0$$eCorresponding author
000889218 1112_ $$aEBRAINS Infrastructure Training$$cOnline$$d2020-11-17 - 2020-11-19$$wOnline
000889218 245__ $$a2nd Elephant User Workshop: Accelerate Structured and Reproducible Data Analysis in Electrophysiology
000889218 260__ $$c2020
000889218 3367_ $$2BibTeX$$aCONFERENCE
000889218 3367_ $$2DRIVER$$aconferenceObject
000889218 3367_ $$2DataCite$$aEvent
000889218 3367_ $$0PUB:(DE-HGF)5$$2PUB:(DE-HGF)$$aConference / Event$$bevent$$mevent$$s1610379436_24638
000889218 3367_ $$2ORCID$$aOTHER
000889218 3367_ $$031$$2EndNote$$aGeneric
000889218 520__ $$aThis event delves into challenges in the reproducibility of neuroscience workflows dealing with classical electrophysiological activity data, such as spiking data or local field potentials, from experiment or simulation.The training will cover the complete cycle from generating structured and consistent data and metadata, accessing the data, pre-processing, setting up analysis workflows, up to the tracking of the provenance of the analysis results. In this context, the e-infrastructure services of EBRAINS offer a mature data, software and compute services ecosystem with community-driven tools developed in the framework of the Human Brain Project. In the first part of the workshop, participants will be trained in the use of tools covering the following topics:    reading and manipulating electrophysiology data in Python using Neo [1]    analysis of such data using Elephant [2]    best practices for integrating metadata into your workflow to aid the analysis process    best practices for structuring analysis results    tracking data analysis pipelines using the HBP Knowledge Graph [3]    collaboration and sharing documents using the HBP Collaboratory [4] In the second part of the workshop, participants will work together with a tutor in small groups, on their own data and on particular personal interests in the scope of the workshop. To this end, participants are asked to provide a small abstract describing the data set they would like to bring and work on (contents of the dataset, data format, data size...) and the topic they are interested in. The latter may, for example, be related to: annotating the dataset with metadata for collaboration and sharing, working with the dataset in the Neo framework, or performing a certain kind of analysis with the data set. The goal of each group is to get started addressing the topic, identify solutions together with the tutors, and implement a first prototype of the required functionality.
000889218 536__ $$0G:(DE-HGF)POF3-574$$a574 - Theory, modelling and simulation (POF3-574)$$cPOF3-574$$fPOF III$$x0
000889218 536__ $$0G:(DE-HGF)POF3-571$$a571 - Connectivity and Activity (POF3-571)$$cPOF3-571$$fPOF III$$x1
000889218 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$x2
000889218 7001_ $$0P:(DE-Juel1)171568$$aDavison, Andrew$$b1
000889218 7001_ $$0P:(DE-Juel1)178793$$aUlianych, Danylo$$b2$$ufzj
000889218 7001_ $$0P:(DE-Juel1)161295$$aSprenger, Julia$$b3$$ufzj
000889218 7001_ $$0P:(DE-Juel1)180365$$aKöhler, Cristiano$$b4$$ufzj
000889218 7001_ $$0P:(DE-Juel1)171572$$aGutzen, Robin$$b5$$ufzj
000889218 7001_ $$0P:(DE-Juel1)176920$$aKleinjohann, Alexander$$b6$$ufzj
000889218 7001_ $$0P:(DE-Juel1)171932$$aStella, Alessandra$$b7$$ufzj
000889218 7001_ $$0P:(DE-Juel1)179059$$aJurkus, Regimantas$$b8$$ufzj
000889218 7001_ $$0P:(DE-Juel1)176777$$aEssink, Simon$$b9$$ufzj
000889218 7001_ $$0P:(DE-Juel1)178725$$aBouss, Peter$$b10$$ufzj
000889218 7001_ $$0P:(DE-Juel1)144168$$aGrün, Sonja$$b11$$ufzj
000889218 8564_ $$uhttps://www.humanbrainproject.eu/en/education/participatecollaborate/infrastructure-events-trainings/2nd-elephant-user-workshop/
000889218 909CO $$ooai:juser.fz-juelich.de:889218$$pec_fundedresources$$pVDB$$popenaire
000889218 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144807$$aForschungszentrum Jülich$$b0$$kFZJ
000889218 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)178793$$aForschungszentrum Jülich$$b2$$kFZJ
000889218 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)161295$$aForschungszentrum Jülich$$b3$$kFZJ
000889218 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)180365$$aForschungszentrum Jülich$$b4$$kFZJ
000889218 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)171572$$aForschungszentrum Jülich$$b5$$kFZJ
000889218 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176920$$aForschungszentrum Jülich$$b6$$kFZJ
000889218 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)171932$$aForschungszentrum Jülich$$b7$$kFZJ
000889218 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)179059$$aForschungszentrum Jülich$$b8$$kFZJ
000889218 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176777$$aForschungszentrum Jülich$$b9$$kFZJ
000889218 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)178725$$aForschungszentrum Jülich$$b10$$kFZJ
000889218 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144168$$aForschungszentrum Jülich$$b11$$kFZJ
000889218 9131_ $$0G:(DE-HGF)POF3-574$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vTheory, modelling and simulation$$x0
000889218 9131_ $$0G:(DE-HGF)POF3-571$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vConnectivity and Activity$$x1
000889218 9141_ $$y2020
000889218 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000889218 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x1
000889218 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x2
000889218 980__ $$aevent
000889218 980__ $$aVDB
000889218 980__ $$aI:(DE-Juel1)INM-6-20090406
000889218 980__ $$aI:(DE-Juel1)INM-10-20170113
000889218 980__ $$aI:(DE-Juel1)IAS-6-20130828
000889218 980__ $$aUNRESTRICTED
000889218 981__ $$aI:(DE-Juel1)IAS-6-20130828